Comma.ai Delivers a Driver-Assist System That Rivals Super Cruise and Autopilot

When George Hotz first shook the foundation of a massive global industry, he was only 17 years old. His feat: He was the first person to unlock an iPhone, thereby enabling its use across wireless networks other than AT&T. Roughly a decade later, Hotz is on the verge of upending another industry.

First drawn to self-driving technology after conversations with Elon Musk about potential work on Tesla’s Autopilot, Hotz instead chose to challenge the auto industry by forging his own path. The results of his efforts became apparent last week when his startup, Comma.ai, showcased the latest iteration of an advanced driver-assist system unlike anything else on the road.

CloudQuant Thoughts: Utilizing ML, AI and lots of data gathered through early adopters using its beta model as well as its Panda Dongle and its Chffr app for mobile phones, George Hotz’ company has been able to create an impressive automated driving system that connects to most new Hondas and Toyotas, and some Acuras and GM models. The cars must have lane assist and enhanced cruise control but with just that technology connected to an Android phone using OpenPilot your car can autodrive as well as a Tesla for under $1000. That is pretty impressive for a small firm and it is all thanks to lots and lots of Deep Machine Learning.
If you want more information watch this TechCrunch launch discussion from 2016, the back and forth at the end goes into a little more detail as to why he thinks his method is the best way to move automated driving forwards.

How decision Trees work by Brandon Rohrer of Facebook

Decision trees are one of my favorite models. They are simple, and they are powerful. In fact most high performing Kaggle entries are a combination of XGBoost, which is variant of decision tree, and some very clever feature engineering.

The concept behind decision trees is refreshingly straightforward. Imagine creating a data set by recording the time you left your house, and noting whether you arrived at work on time. Looking at it, you can see that for the most part, departure times before 8:15 result in punctuality, and departure times after 15 result in tardiness.

CloudQuant Thoughts: A very nicely put together introduction video. Well worth 15 minutes of your time (or 10 if, like me, you watch all YouTube videos at 1.5x speed!)

A case for less human bots

In March, Bank of America launched its new in-app AI-powered assistant. Named Erica, the bot presumably takes its name from “America.” Eric surely could have sufficed as well, but giving a customer service bot a female moniker, and voice, has become common practice.

Think Alexa, Cortana, and Siri. Even loading up an on-screen webchat is likely to bring up a female-sounding name.

CloudQuant Thoughts: Why are most AI driven Bots female? Why are we comfortable having female AI helpers? Something seems off. It just feels wrong. If we ever create a Bot at work we are going to take a more C3PO type approach – one without gender, race, etc.

Google Wants to Dominate AI in 2018. Here’s Why

Let’s get this out of the way: ever since the 2001 movie A.I. (and actually way before then, too), people have believed that artificial intelligence would soon emerge in the form of a race of robots that imitate human behavior until they become so smart and successful that they ultimately overpower the human race.

We envision artificial intelligence as computers taking on lives of their own and using their brains to pull all sorts of shenanigans.
2018-07-23: Read the full story.

Eventus Systems Appoints First Outside Directors with Three Industry Veterans

Eventus Systems, Inc., a provider of innovative regtech software solutions for the capital markets, today announced that it has appointed three highly acclaimed industry veterans as the first outside directors on its Board. The newly constituted Board will meet for the first time this afternoon. The new Board members are Kim Taylor, former President, Clearing and Post-Trade Services at CME Group; Fred Hatfield, former Commissioner of the U.S. Commodity Futures Trading Commission; and D. Keith Ross, Jr., Executive Chairman of PDQ Enterprises, parent company to broker-dealer and independent Alternative Trading System CODA Markets (formerly PDQ ATS), and former CEO of GETCO LLC.

Eventus CEO Travis Schwab said: “We are truly fortunate to add three of the best minds in the industry to our Board as we continue growing our presence in the marketplace and helping our clients solve some of the most vexing challenges in trade surveillance, compliance and risk management. The expansion of our Board with outside directors is a natural step for us as we mature as an organization, but we are especially pleased to benefit from the strategic counsel of these talented, highly experienced professionals – Kim with her outstanding futures, clearing and risk management background; Fred with his derivatives regulatory and energy market expertise, and Keith with his incredible grounding in equities and market structure.”
2018-06-28: Read the full story.

CloudQuant Thoughts: CloudQuant’s parent company uses Eventus technology. We are proud to be an early adopter of their Regtech tools.

The skill of feature engineering — crafting data features optimized for machine learning — is as old as data science itself. But it’s a skill I’ve noticed is becoming more and more neglected. The high demand for machine learning has produced a large pool of data scientists who have developed expertise in tools and algorithms but lack the experience and industry-specific domain knowledge that feature engineering requires. And they are trying to compensate for that with better tools and algorithms. However, algorithms are now a commodity and don’t generate corporate IP.
2018-07-21 00:00:00 Read the full story.

CloudQuant Thoughts: We have observed this when it comes to stock trading. Simply throwing numbers at an ML algo will not work. Formatting those numbers and normalizing them is better but still will not work. What is needed is knowledge of the “context of the data”. And with model production, having a deep knowledge of the context can save you lots of time and CPU cycles.

Microsoft adds AI and IoT cautionary language to its earnings

Microsoft reported its Q4 2018 earnings yesterday, with highlights like surpassing $100 billion in revenue for the fiscal year, all three operating groups seeing double-digit year-over-year growth, and as a result the stock soaring past $800 billion in value. All of that meant a smaller tidbit slipped through: three additions and three minor changes made to the earnings release.

The Forward-Looking Statements section of the release has had the same boilerplate for years:

Statements in this release that are “forward-looking statements” are based on current expectations and assumptions that are subject to risks and uncertainties. Actual results could differ materially because of factors such as:

This is then followed by 24 factors. In this past quarter’s release, there were 27 factors. Here are the three new ones:

the development of the internet of things presenting security, privacy, and execution risks;

issues about the use of artificial intelligence in our offerings that may result in competitive harm, legal liability, or reputational harm; and

damage to our reputation or our brands that may harm our business and operating results.

How The Tech Community Is Leading The War Against Development Of Lethal Autonomous Weapons

Of late, there have been many arguments against killer robots and lethal autonomous weapons but none have been more potent than the recent news about more than 2,400 technology leaders calling for an open ban on the development of lethal autonomous weapons. One of the biggest voices in this debate has been Tesla’s Elon Musk, who has rallied against the use of killer robots. In a recent conference in Stockholm more than 2,400 individuals and 150 companies from 90 different countries vowed to play no part in the construction, trade, or use of autonomous weapons in a pledge signed on Wednesday at the 2018 International Joint Conference on Artificial Intelligence in Sweden.

Max Tegmark, president of the Future of Life Institute and one of the supporters of the ban against development of LAWS, “AI has huge potential to help the world — if we stigmatise and prevent its abuse. AI weapons that autonomously decide to kill people are as disgusting and destabilising as bioweapons, and should be dealt with in the same way”. According to a statement released by the body, the decision to take a human life should never be delegated to a machine since LAWS engaging targets without human intervention – would be dangerously destabilising for every country and individual.
2018-07-20 13:23:37+00:00 Read the full story.

Elon Musk, Google’s DeepMind co-founders and others promise never to make killer robots

Tesla and SpaceX billionaire Elon Musk and all three of the co-founders of Google’s DeepMind are among the thousands of individuals and almost 200 organizations who have publicly committed not to develop, manufacture or use killer robots.

“We the undersigned agree that the decision to take a human life should never be delegated to a machine,” reads the pledge published Wednesday and organized by the Boston nonprofit Future of Life, an organization that researches the benefits and risks of artificial intelligence along with other existential issues related to advancing technology.

CloudQuant Thoughts: In these last two stories the leaders in AI are starting to take the steps that their employees have been calling for in recent months.

Below the Fold…

Top 20 Python AI and Machine Learning Open Source Projects

Getting into Machine Learning and AI is not an easy task. Many aspiring professionals and enthusiasts find it hard to establish a proper path into the field, given the enormous amount of resources available today. The field is evolving constantly and it is crucial that we keep up with the pace of this rapid development. In order to cope with this overwhelming speed of evolution and innovation, a good way to stay updated and knowledgeable on the advances of ML, is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals.

“The purpose is to help the data scientist be productive and at the same time give the IT folks the control and automation they need to be successful with large scale deployments, machine learning, and artificial intelligence,” said Mathew Lodge, SVP products and marketing, Anaconda.

The company worked with NVIDIA to build out support for running the platform at scale on cloud native infrastructure, Lodge explained.
2018-07-17 00:00:00 Read the full story.

VIDEO: What’s the Difference between Cognitive Computing and AI?

At Data Summit 2018, Hadley Reynolds, co-founder of the Cognitive Computing Consortium, presented a keynote looking at the meaning of AI and cognitive computing.

In particular, he considered what each of the terms AI and cognitive computing really mean and how they differ.

By 2016, IBM was starting to use the term AI, and in 2017, IBM “really” used the term, and so now everybody is talking about AI, said Reynolds. Given this, he noted, some might wonder why the consortium is still talking about cognitive computing.

“This is what we propose from the standpoint of our conversation,” said Reynolds: “That the fundamental differentiation is the extent to which the machine can emulate human thought processes, behaviors, and interactions.”
2018-07-17 00:00:00 Read the full story.

Why “data for good” lacks precision. – Towards Data Science

Let’s start by asking what do we mean by “data”? I will constrain the scope of our discussion by defining “data” as referring to a project that extracts information from an existing dataset or involves the collection of new/additional data. This often entails data collection, cleaning and/or the application of statistical tools and/or machine learning models. This work can also involve building technical tools for data collection or model deployment.

“Data for good” refers to a subset of data projects. “Data for good” is an odd descriptor because it implies that some data is not being used for good or is at least ambivalent in the nature of its application. The subjective nature of the word “good” as a qualifier means that there may be multiple valid definitions used at the same time.

I have frequently seen four criteria used to qualify a project as falling under the “data for good” umbrella:

The end recipient of the data product is a non-profit or government agency.

Skilled volunteer/s develop and deliver the data product.

Data tools are provided to the organization/individual for free or at a heavily subsidized amount.

Educational training to improve the data skills of an underserved community

Plotting decision boundaries in 3D — Logistic regression and XGBoost

Though there’re already quite a few learning resources out there, I believe a nice interactive 3D plot will definitely help the readers gain intuition for ML models. Here I pick two models for analysis: Logistic regression, which is easy to train and deploy, and it’s commonly used in many areas; XGBoost, one of the leading ML algorithms from the gradient boosting tree family (Gradient boosting, LightGBM, etc.).

Machine Learning: A Micro Primer with a Lawyer’s Perspective

What Is Machine Learning? I am partial towards this definition by Nvidia:

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”

The first step to understanding machine learning is understanding what kinds of problems it intends to solve, based on the foregoing definition. It is principally concerned with mapping data to mathematical models — allowing us to make inferences (predictions) about measurable phenomena in the world. From the machine learning model’s predictions, we can then make rational, informed decisions with increased empirical certainty.

Corvil Taps AI For ’12 Steps of Big Data Grief’

Corvil, which provides data analytics for electronic trading, is using machine learning and artificial intelligence to help clients make sense of huge volumes of data.

David Murray, chief business development officer at Corvil, told Markets Media that market participants are looking for help in using data in three areas – client intelligence, measuring execution quality, and venue analysis. They find it difficult to analyse huge amounts of data and determine which signals are most important.

Murray told Markets Media: “Clients are struggling with what we call the ’12 steps of Big Data grief’ while Corvil has rich data covering the order lifecycle. The Intelligence Hub has been developed over three years to provide analytics that can be used easily in electronic trading.”

Why The Best Way To Prepare For AI Is By Studying Past Technological Revolutions

Scientific and technological advancements have always had significant impacts on human lives over the course of history. Artificial intelligence, as a major technological force, has already started exhibiting its impact at a global level. However, this is just the beginning. The impending impact is going to be more deep-rooted, more disruptive and more transformative than we have witnessed in history. Every aspect of human civilisation is going to get affected and eventually transformed — be it academia, corporations, social institutions or individuals.
2018-07-19 12:01:08+00:00 Read the full story.

Deep Learning and Artificial Intelligence

Artificial intelligence (AI) is in the midst of an undeniable surge in popularity, and enterprises are becoming particularly interested in a form of AI known as deep learning.

According to Gartner, AI will likely generate $1.2 trillion in business value for enterprises in 2018, 70 percent more than last year. “AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs),” said John-David Lovelock, research vice president at Gartner.

Can AI help brewers predict how new beer varieties will taste? Carlsberg says “probably”

Carlsberg now is leading the way in bringing artificial intelligence (AI) to one of the world’s oldest industries. The Beer Fingerprinting Project will help researchers at Carlsberg, the fourth-largest brewing company in the world with 140 beverage brands in 150 countries, use advanced sensors and analytics to more quickly map out and predict flavors. And it’s all aided by a move to the cloud to help speed along the company’s “Sail’22” growth strategy and better contend with increased competitive pressure.

AI-assisted art moves from pixels to paintbrushes

This week, ten winners of the third annual international RobotArt competition, which tasks contestants with designing artistically inclined AI, were selected from more than 100 submissions entered by 19 teams. Each work of art was voted on publicly and judged by a panel of artists, technologists, and critics on how well the team adhered to the spirit of the competition: “creating something beautiful using a physical brush and robotics and [sharing] what they learned with others.”

Top 10 Free Books And Resources For Learning TensorFlow

TensorFlow, the open source software library developed by the Google Brain team, is a framework for building deep learning neural networks. It is also considered one of the best ways to build deep learning models by machine learning practitioners across the globe. In deep learning models, which rely on a lot of data and computing resources, TensorFlow is used significantly.

Given its flexible architecture for easy deployment on various platforms such as CPUs, GPUs and TPUs, TensorFlow remains one of the favourite libraries to get into ML. Its huge popularity also means that tech enthusiasts are on a constant lookout to learn more and work more with this library. While there are many tutorials, books, projects, videos, white papers, and other resources available, we bring you these 10 free resources to get started with TensorFlow and get your concepts clear.

Tutorial By TensorFlow (Website)

TensorFlow White Paper (Paper)

Stanford Course On Tensorflow For Deep Learning Research (PPT)

First Contact With TensorFlow Get Started With Deep Learning Programming By Jordi Torres (EBook)

IBM’s AI watermarking method protects models from theft and sabotage

What if machine learning models, much like photographs, movies, music, and manuscripts, could be watermarked nearly imperceptibly to denote ownership, stop intellectual property thieves in their tracks, and prevent attackers from compromising their integrity? Thanks to IBM’s new patent-pending process, they can be.

Marc Ph. Stoecklin, manager of cognitive cybersecurity intelligence at IBM : “For the first time, we have a [robust] way to prove that someone has stolen a model,” Stoecklin said. “Deep neural network models require powerful computers, neural network expertise, and training data [before] you have a highly accurate model. They’re hard to build, and so they’re prone to being stolen. Anything of value is going to be targeted, including neural networks.”

More than half of hedge funds now using AI technology

Research services provider BarclayHedge found that hedge funds are now leaning towards AI technology for the investment process. Fifty-eight percent of hedge funds are now claiming it has been used for more than three years.

Despite 56% of respondents using AI and machine learning to inform investment decisions and 67% using it to generate trading ideas, the survey suggests that hedge funds are not quite ready to allow AI or machine learning to execute trades on their behalf.

Intro to Machine Learning for Finance (Part 1) — Alpaca Blog

There has been increasing talk in recent years about the application of machine learning for financial modeling and prediction. But is the hype justified? Is machine learning worth investing time and resources into mastering?

This series will be covering some of the design decisions and challenges to creating and training neural networks for use in finance, from simple predictive models to the use of ML to create specialised trading indicators and statistics — with example code and models along the way.

Every AI startup is not an AI startup – Hacker Noon

Take 100 startups and ask them “Who is an AI startup?” I am confident the majority will say they are or at least will attach AI to their narrative.

Here is the crucial difference — AI systems are becoming more intelligent through time and getting smarter by “consuming” and analyzing more data (It’s is like a kid becoming more intelligent and smart during several years as the kid is studying new things at school).
2018-07-23 11:31:01.604000+00:00 Read the full story.

3 basic approaches in Bag of Words which are better than Word Embeddings

In the-state-of-art of the NLP field, Embedding is the success way to resolve text related problem and outperform Bag of Words (BoW). Indeed, BoW introduced limitations such as large feature dimension, sparse representation etc. For word embedding, you may check out my previous post. Should we still use BoW?

Doing Good Data Science

The hard thing about being an ethical data scientist isn’t understanding ethics. It’s the junction between ethical ideas and practice. It’s doing good data science.

There has been a lot of healthy discussion about data ethics lately. We want to be clear: that discussion is good, and necessary. But it’s also not the biggest problem we face. We already have good standards for data ethics. The ACM’s code of ethics, which dates back to 1993, is clear, concise, and surprisingly forward-thinking; 25 years later, it’s a great start for anyone thinking about ethics. The American Statistical Association has a good set of ethical guidelines for working with data. So, we’re not working in a vacuum.

Oracle Study Finds 93% of People Would Trust Orders from a Robot at Work

People are ready to take instructions from robots at work according to a new Oracle study. However, the survey of 1,320 U.S. HR leaders and employees revealed that while people are ready to embrace artificial intelligence (AI) at work, and understand that the benefits go far beyond automating manual processes, organizations are not doing enough to help their employees embrace AI and that will result in reduced productivity, skillset obsolescence and job loss.

The study, titled “AI at Work,” identified a large gap between the way people are using AI at home and at work. While 70% of people are using some form of AI in their personal life, only 6% of HR professionals are actively deploying AI and only 24% of employees are currently using some form of AI at work. To determine why there is such a gap in AI adoption when people are clearly ready to embrace AI at work (93% would trust orders from a robot), the study examined HR leader and employee perceptions of the benefits of AI, the obstacles preventing AI adoption and the business consequences of not embracing AI. All respondents agreed that AI will have a positive impact on their organizations and when asked about the biggest benefit of AI, HR leaders and employees both said increased productivity.

The World is Changing Fast. Worry About It Or Profit From It

I discovered something interesting today. 15 years ago, on this exact date, I updated my bank passbook for the last time. For the young ones among us, a bank passbook is a small booklet that banks used to issue to all its account holders. Inside, the passbook would list the amount of cash you have in your bank account. Oh, I remember those days.

My mom will bug me to keep my passbook updated to make sure the correct amount of cash was reflected. To do that, we would have to go down to a bank branch, stand a queue, and get a bank teller to update our booklets for us. It would be years later before automated machines took over that menial task.

RealNetworks, the Seattle company best known for pioneering streaming media in the early days of the web, is deploying a surprising new product today. The company says it will offer a new facial recognition technology, called SAFR, for free to K-12 schools to help upgrade their on-site security systems.

SAFR can be used with the same cameras that traditional surveillance systems to recognize students, staff, and people visiting schools. RealNetworks says that in addition to security, the tool can also help with record-keeping and “campus monitoring.”

UCI machine learning dataset repository is something of a legend in the field of machine learning pedagogy. It is a ‘go-to-shop’ for beginners and advanced learners alike. It is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited “papers” in all of computer science.

That said, navigating the portal can be bit frustrating and time consuming as there is no simple intuitive API or download link for the data set you are interested in. You have to hop around multiple pages to go to the raw data set page that you are looking for. Also, if you are interested in particular type of ML task (regression or classification for example) and want to download all datasets corresponding to that task, there is no simple command to accomplish such. I am glad to introduce a simple and intuitive API for UCI ML portal, where users can easily look up a data set description, search for a particular data set they are interested, and even download datasets categorized by size or machine learning task.

Evolution of a salesman: A complete genetic algorithm tutorial for Python

Drawing inspiration from natural selection, genetic algorithms (GA) are a fascinating approach to solving search and optimization problems. While much has been written about GA (see: here and here), little has been done to show a step-by-step implementation of a GA in Python for more sophisticated problems. That’s where this tutorial comes in! Follow along and, by the end, you’ll have a complete understanding of how to deploy a GA from scratch.

In this tutorial, we’ll be using a GA to find a solution to the traveling salesman problem (TSP). The TSP is described as follows: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?”

Data Science for Startups: Model Services

In order for data scientists to be effective at a startup, they need to be able to build services that other teams can use, or that products can use directly. For example, instead of just defining a model for predicting user churn, a data scientist should be able to set up an endpoint that provides a real-time prediction for the likelihood of a player to churn. Essentially, the goal is to provide a model as a service, or function call that products can use directly.

This type of capability, providing model predictions with sub-millisecond latency, can be categorized as providing models as a service. AWS lambda provides a great way of implementing these capabilities, but does require some set up to get working with common ML libraries. The goal of this post is to show how to use AWS lambda to set up an endpoint that can provide model predictions.

A new study shows that tech CEOs are optimistic about the future, even if they still don’t understand millennials

several top tech firms that have been betting on and investing in artificial-intelligence technology. Richard Drew/AP

Tech industry CEOs are bullish on the future of their companies, the sector, and artificial intelligence.

But they’re worried about the spread of nationalism, cybersecurity — and millennials.

Those are some of the key takeaways from a new report by KPMG. After surveying more than 1,000 CEOs from all different sectors and from around the globe, the company zeroed in on the responses of 104 from the tech industry.

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